In [8]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
In [3]:
df=pd.read_csv("winequality1-red.csv")
df
Out[3]:
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
0 7.4 0.700 0.00 1.9 0.076 11.0 34.0 0.99780 3.51 0.56 9.4 5
1 7.8 0.880 0.00 2.6 0.098 25.0 67.0 0.99680 3.20 0.68 9.8 5
2 7.8 0.760 0.04 2.3 0.092 15.0 54.0 0.99700 3.26 0.65 9.8 5
3 11.2 0.280 0.56 1.9 0.075 17.0 60.0 0.99800 3.16 0.58 9.8 6
4 7.4 0.700 0.00 1.9 0.076 11.0 34.0 0.99780 3.51 0.56 9.4 5
... ... ... ... ... ... ... ... ... ... ... ... ...
1594 6.2 0.600 0.08 2.0 0.090 32.0 44.0 0.99490 3.45 0.58 10.5 5
1595 5.9 0.550 0.10 2.2 0.062 39.0 51.0 0.99512 3.52 0.76 11.2 6
1596 6.3 0.510 0.13 2.3 0.076 29.0 40.0 0.99574 3.42 0.75 11.0 6
1597 5.9 0.645 0.12 2.0 0.075 32.0 44.0 0.99547 3.57 0.71 10.2 5
1598 6.0 0.310 0.47 3.6 0.067 18.0 42.0 0.99549 3.39 0.66 11.0 6

1599 rows × 12 columns

In [4]:
# See the number of rows and columns
print("Rows, columns: " + str(df.shape))
# See the first five rows of the dataset
df.head()

Rows, columns: (1599, 12)
Out[4]:
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
0 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4 5
1 7.8 0.88 0.00 2.6 0.098 25.0 67.0 0.9968 3.20 0.68 9.8 5
2 7.8 0.76 0.04 2.3 0.092 15.0 54.0 0.9970 3.26 0.65 9.8 5
3 11.2 0.28 0.56 1.9 0.075 17.0 60.0 0.9980 3.16 0.58 9.8 6
4 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4 5
In [5]:
# Missing Values
print(df.isna().sum())

fixed acidity           0
volatile acidity        0
citric acid             0
residual sugar          0
chlorides               0
free sulfur dioxide     0
total sulfur dioxide    0
density                 0
pH                      0
sulphates               0
alcohol                 0
quality                 0
dtype: int64
In [6]:
fig = px.histogram(df,x='quality')
fig.show()
In [9]:
corr = df.corr()
matplotlib.pyplot.subplots(figsize=(15,10))
sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [9], in <cell line: 2>()
      1 corr = df.corr()
----> 2 matplotlib.pyplot.subplots(figsize=(15,10))
      3 sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))

NameError: name 'matplotlib' is not defined
In [10]:
# Create Classification version of target variable
df['goodquality'] = [1 if x >= 7 else 0 for x in df['quality']]
# Separate feature variables and target variable
X = df.drop(['quality','goodquality'], axis = 1)
y = df['goodquality']
In [11]:
# See proportion of good vs bad wines
df['goodquality'].value_counts()
Out[11]:
0    1382
1     217
Name: goodquality, dtype: int64
In [12]:
# Normalize feature variables
from sklearn.preprocessing import StandardScaler
X_features = X
X = StandardScaler().fit_transform(X)
In [13]:
# Splitting the data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=0)
In [14]:
from sklearn.metrics import classification_report
from sklearn.tree import DecisionTreeClassifier
model1 = DecisionTreeClassifier(random_state=1)
model1.fit(X_train, y_train)
y_pred1 = model1.predict(X_test)
print(classification_report(y_test, y_pred1))

              precision    recall  f1-score   support

           0       0.96      0.92      0.94       355
           1       0.53      0.73      0.62        45

    accuracy                           0.90       400
   macro avg       0.75      0.83      0.78       400
weighted avg       0.92      0.90      0.90       400
In [15]:
from sklearn.ensemble import RandomForestClassifier
model2 = RandomForestClassifier(random_state=1)
model2.fit(X_train, y_train)
y_pred2 = model2.predict(X_test)
print(classification_report(y_test, y_pred2))

              precision    recall  f1-score   support

           0       0.95      0.97      0.96       355
           1       0.68      0.58      0.63        45

    accuracy                           0.92       400
   macro avg       0.82      0.77      0.79       400
weighted avg       0.92      0.92      0.92       400
In [16]:
feat_importances = pd.Series(model2.feature_importances_, index=X_features.columns)
feat_importances.nlargest(25).plot(kind='barh',figsize=(10,10))
Out[16]:
<AxesSubplot:>
In [20]:
from sklearn.ensemble import AdaBoostClassifier
model3 = AdaBoostClassifier(random_state=1)
model3.fit(X_train, y_train)
y_pred3 = model3.predict(X_test)
print(classification_report(y_test, y_pred3))

              precision    recall  f1-score   support

           0       0.94      0.94      0.94       355
           1       0.51      0.49      0.50        45

    accuracy                           0.89       400
   macro avg       0.72      0.71      0.72       400
weighted avg       0.89      0.89      0.89       400
In [ ]:
In [19]:
from sklearn.ensemble import GradientBoostingClassifier
model4 = GradientBoostingClassifier(random_state=1)
model4.fit(X_train, y_train)
y_pred4 = model4.predict(X_test)
print(classification_report(y_test, y_pred4))

              precision    recall  f1-score   support

           0       0.94      0.94      0.94       355
           1       0.52      0.51      0.52        45

    accuracy                           0.89       400
   macro avg       0.73      0.73      0.73       400
weighted avg       0.89      0.89      0.89       400
In [22]:
# Filtering df for only good quality
df_temp = df[df['goodquality']==1]
df_temp.describe()

# Filtering df for only bad quality
df_temp2 = df[df['goodquality']==0]
df_temp2.describe()
Out[22]:
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality goodquality
count 1382.000000 1382.000000 1382.000000 1382.000000 1382.000000 1382.000000 1382.000000 1382.000000 1382.000000 1382.000000 1382.000000 1382.000000 1382.0
mean 8.236831 0.547022 0.254407 2.512120 0.089281 16.172214 48.285818 0.996859 3.314616 0.644754 10.251037 5.408828 0.0
std 1.682726 0.176337 0.189665 1.415778 0.049113 10.467685 32.585604 0.001808 0.154135 0.170629 0.969664 0.601719 0.0
min 4.600000 0.160000 0.000000 0.900000 0.034000 1.000000 6.000000 0.990070 2.740000 0.330000 8.400000 3.000000 0.0
25% 7.100000 0.420000 0.082500 1.900000 0.071000 8.000000 23.000000 0.995785 3.210000 0.540000 9.500000 5.000000 0.0
50% 7.800000 0.540000 0.240000 2.200000 0.080000 14.000000 39.500000 0.996800 3.310000 0.600000 10.000000 5.000000 0.0
75% 9.100000 0.650000 0.400000 2.600000 0.091000 22.000000 65.000000 0.997900 3.410000 0.700000 10.900000 6.000000 0.0
max 15.900000 1.580000 1.000000 15.500000 0.611000 72.000000 165.000000 1.003690 4.010000 2.000000 14.900000 6.000000 0.0
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